Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Harvard University
- National University of Singapore
- UNIVERSITY OF SURREY
- Nanyang Technological University
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- Singapore University of Technology & Design
- University of Oslo
- Aarhus University
- CRANFIELD UNIVERSITY
- Center for Devices and Radiological Health (CDRH)
- Cranfield University
- Dana-Farber Cancer Institute (DFCI)
- Hong Kong Polytechnic University
- INESC TEC
- Imperial College London
- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
- Oden Institute for Computational Engineering and Sciences
- SUNY University at Buffalo
- UCL;
- University of California
- University of Idaho
- University of Maryland, Baltimore
- University of Texas Rio Grande Valley
- University of Waterloo
- Zintellect
- 16 more »
- « less
-
Field
-
turbulent flow simulations. 3. Background in numerical methods and scientific computing. 4. Proficiency in Python programming and high-performance computing on modern GPU-based platforms
-
with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
-
computational modelling of additive manufacturing and develop high-performance GPU-based CFD solvers. Qualifications • With PhD degree • Strong research experience in developing GPU-based
-
computing environments and GPU computing. Proven experience in weather and climate models development and applications. Experience in machine learning, deep learning, or AI applications for atmospheric
-
with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
-
hardware (e.g., GPUs and/or Non-volatile memory) and data science applications.
-
with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
-
the speed up from using GPUs as well as machine learning techniques, e.g. simulation-based inference. Finally, we will use similar techniques to make a statistical inference of the population of subhaloes by
-
population and comparative genomics to examine genetic diversity, selection, pangenome relationships, and functional conservation. You will also develop reproducible GPU- and CPU-based high-performance
-
team and supported by cutting-edge HPC and GPU infrastructure, you will contribute to internationally leading research, publish in high-impact journals and present your work at major scientific